Senior Data Scientist

Key Skills

Aartificial intelligence, Algorithms, Anomaly detection, Apache Cassandra, Apache Hadoop, Applied Machine Learning, Big Data Technology, C Programming, Clustering, CNTK, Containerization, Data Architecture, Data Engineering, Data Mining, Data science techniques, Decision trees, Deep Learning, Design, Distributed systems, Docker, Druid, GoLang, Java Programming, Keras software library, Kubeflow, Kubernetes-K8s, Large scale deployment, MxNet, Neural Networks, NoSQL, Object-Oriented programming (OO languages), PySpark, Python Programming, PyTorch, R Programming, R Studio, REDIS, Regression Analysis, RNNs, SAS, Scikit-learn, script, SPARK Programming, SQL, TensorFlow

Job Description

Senior Data Scientist

Primary Skills

• Classification (Decision Trees, SVM), Distance (Hamming Distance, Euclidean Distance, Manhattan Distance), Forecasting (Exponential Smoothing, ARIMA, ARIMAX), Great Expectation, Evidently AI, Hypothesis Testing, ML Frameworks (TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet), Probabilistic Graph Models, Python/PySpark, R/ R Studio, Regression (Linear, Logistic), SAS/SPSS, Statistical analysis and computing, Tools(KubeFlow, BentoML), T-Test, Z-Test

Specialization

• Data Science Advanced: Data Specialist

Job requirements

• Job Responsibilities: As the Data Science lead, you are expected to engineer, develop, support, and deliver ML models using real time network data to enable decision making and drive insights for teams across Planning, Engineering, Operations and Assurance groups for a leading Telecom client.
• Collaborate with team to drive the technical roadmap and guide development and implementation of new data driven business solution
• Architecture and design of AI Platform services including Machine Learning Engines, In Memory Computing Systems, Streaming Computing Systems, Distributed Data Systems and etc, in Spark, Java, and Python.
• Coordinate the implementation among development teams to ensure system performance, security, scalability and availability.

Skills:

• 12 or more years of relevant work experience
• 2 years of experience in developing and deploying products using Docker, Kubernetes, and the containerization technology stack
• 3 years of experience in development of advanced machine learning and deep learning models such as RNN, LSTM, and specifically Graph Neural Networks(GNN)
• 3 years of experience in modern data mining and data science techniques (e.g., regressions, decision trees, ensemble algorithms, neural networks, time series analytics, clustering, anomaly detection, text analytics, etc.)
• Data Wrangling: The candidate must have proficient data wrangling skills with Python, SQL, and other data processing tools/scripts.
• Software Engineering background: The candidate must be proficient in Python and at least one object-oriented programming language (Java, Golang, C, etc.)
• Domain Knowledge - Candidate with background in telecom preferred.
• Distributed Systems: practical experience with NoSQL data platforms (e.g., Druid, Cassandra) and caching technologies like Redis is a plus.
• Expert in developing large scale, enterprise class distributed system or subsystems that require high availability, low-latency, & strong data consistency computing.
• Experience with Big Data and analytics in general leveraging technologies like Hadoop, Spark, and MapReduce